Abstract
Bioelectrochemical systems (BES) are promising technologies to convert organic compounds in wastewater to electrical energy through a series of complex physical-chemical, biological and electrochemical processes. Representative BES such as microbial fuel cells (MFCs) have been studied and advanced for energy recovery. Substantial experimental and modeling efforts have been made for investigating the processes involved in electricity generation toward the improvement of the BES performance for practical applications. However, there are many parameters that will potentially affect these processes, thereby making the optimization of system performance hard to be achieved. Mathematical models, including engineering models and statistical models, are powerful tools to help understand the interactions among the parameters in BES and perform optimization of BES configuration/operation. This review paper aims to introduce and discuss the recent developments of BES modeling from engineering and statistical aspects, including analysis on the model structure, description of application cases and sensitivity analysis of various parameters. It is expected to serves as a compass for integrating the engineering and statistical modeling strategies to improve model accuracy for BES development.
Highlights
Bioelectrochemical systems (BES) are emerging technologies that apply microorganisms to transform chemical energy in wastewater to electrical energy through multiple microbial-electrochemical reactions [1,2,3,4]
The above ordinary differential equations (ODEs) stereotypes are further developed for other BES, such as microbial desalination cells (MDCs), osmotic-microbial fuel cell (OsMFC), pressure-retarded osmosis/microbial electrolysis cell (PRO-microbial electrolysis cells (MECs)), and membrane bioelectrochemical reactors (MBERs)
Yuan et al [77] constructed an engineering model to successfully predict the dynamic volume profile of feed and draw solution with root mean square error (RMSE) smaller than 2.5%; the RMSE for a MEC model was relatively high (13%–23.6%), the predicted output was still acceptable because the error came from overestimation of organics in the model, which was related to microbial degradation of organics in anode featured with relatively higher unstableness and delayed response of biological factors than other physical attributes
Summary
Bioelectrochemical systems (BES) are emerging technologies that apply microorganisms to transform chemical energy in wastewater to electrical energy through multiple microbial-electrochemical reactions [1,2,3,4]. To help satisfy the increasing needs for mathematical models to achieve better development of BES for wastewater treatment and energy recovery, this review aims to summarize and discuss different modeling strategies for BES in the categories of engineering models and statistical models. It is significantly different from a recent review paper of mathematical models that focuses on overview of model studies [19].
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